Evaluating the performance of autonomous vehicle planning algorithms necessitates simulating long-tail traffic scenarios. Traditional methods for generating safety-critical scenarios often fall short in realism and controllability. Furthermore, these techniques generally neglect the dynamics of agent interactions. To mitigate these limitations, we introduce a novel closed-loop simulation framework rooted in guided diffusion models. Our approach yields two distinct advantages: 1) the generation of realistic long-tail scenarios that closely emulate real-world conditions, and 2) enhanced controllability, enabling more comprehensive and interactive evaluations. We achieve this through novel guidance objectives that enhance road progress while lowering collision and off-road rates. We develop a novel approach to simulate safety-critical scenarios through an adversarial term in the denoising process, which allows the adversarial agent to challenge a planner with plausible maneuvers, while all agents in the scene exhibit reactive and realistic behaviors. We validate our framework empirically using the NuScenes dataset, demonstrating improvements in both realism and controllability. These findings affirm that guided diffusion models provide a robust and versatile foundation for safety-critical, interactive traffic simulation, extending their utility across the broader landscape of autonomous driving. For additional resources and demonstrations, visit our project page at https://safe-sim.github.io.

Traffic simulation plays a crucial role in evaluating and improving autonomous driving planning systems. After being deployed on public roads, autonomous vehicles need to interact with human road participants with different social preferences (e.g., selfish or courteous human drivers). To ensure that autonomous vehicles take safe and efficient maneuvers in different interactive traffic scenarios, we should be able to evaluate autonomous vehicles against reactive agents with different social characteristics in the simulation environment. We propose a socially-controllable behavior generation (SCBG) model for this purpose, which allows the users to specify the level of courtesy of the generated trajectory while ensuring realistic and human-like trajectory generation through learning from real-world driving data. Specifically, we define a novel and differentiable measure to quantify the level of courtesy of driving behavior, leveraging marginal and conditional behavior prediction models trained from real-world driving data. The proposed courtesy measure allows us to auto-label the courtesy levels of trajectories from real-world driving data and conveniently train an SCBG model generating trajectories based on the input courtesy values. We examined the SCBG model on the Waymo Open Motion Dataset (WOMD) and showed that we were able to control the SCBG model to generate realistic driving behaviors with desired courtesy levels. Interestingly, we found that the SCBG model was able to identify different motion patterns of courteous behaviors according to the scenarios.